In the field of imaging, and more particularly medical imaging, image region segmentation methods are used to isolate subsets of an image corresponding to specific objects such as an artery, the heart, kidneys or any other such anatomical structure. These images may be obtained by various means such as, for example, Magnetic Resonance Imaging (MRI), Computerized Tomography (CT) or any other such medical imaging system. Segmentation, or feature extraction, is an important requirement for any medical imaging application. When a radiologist or a physician looks at an image obtained from a medical imaging system, such as from an MRI, CT or any other similar system, he or she mentally isolates the structure relevant to the desired diagnosis. If the structure has to be measured or visualized by the computer, the radiologist or physician has to identify the structure on the original images using a computer and program.
There are two major classes of region segmentation methods. The first class of region segmentation methods is binary segmentation. Binary segmentation determines for every voxel in the image, using a specific property or function and threshold values, whether or not that point belongs to an anatomical structure. The second class of region segmentation methods, gray-level segmentation, determines for every voxel in the image, using a specific property or function, the “level of confidence” for which that point belongs to an anatomical structure.
Gray-level segmentation methods have the advantage of avoiding a priori knowledge of the threshold values by creating a “connectivity map” or membership that associates a level of confidence to each voxel, creating a number of solutions that depend on the minimal level of confidence desired. The user can then simply set the threshold for the minimal level of confidence of the voxels to be displayed and can interactively raise or lower the threshold according to the obtained image.
There are, however, situations in which current gray-level segmentation methods are not optimal. For instance, when trying to segment a large blood vessel, the algorithm will segment the vessel only partially and include some bone structure because its density is very close to the density of the large blood vessel. Furthermore, gray-level segmentation algorithms using fuzzy logic base the membership values purely on the density of the image voxels and the connectedness to the seed point. Accordingly, a voxel who's density is identical to the seed point and to which there is a direct path from the seed point, such as for example an Arterio-Venous Malformation, will have a very high membership value. This characteristic makes it difficult to properly segment the desired structure where distinct structures with similar densities exist.
It is the object of the present invention to provide a system and method which obviates or mitigates the abovementioned disadvantages.
In accordance with the invention, there is provided a method of segmenting an image of a structure stored as a set of spatially related data points representing variations in a predetermined parameter, said method comprising the steps of selecting a seed point within the structure to be segmented, assigning to each of the data points a value of connectivity indicative of the confidence that respective areas of the data points are part of the same structure as said seed point, said value of connectivity including a function of the distance of the respective point from said seed point, establishing a threshold value for said level of connectivity and selecting for display data points meeting said threshold value.